Quick-Start Scenarios
Click any scenario to auto-fill the form with a realistic context.
Peak-hour Chicago → LA
Morning rush departure, Friday
Overnight LA → Chicago
Late-night departure, off-peak
Same-day Chicago → Dallas
Midday Tuesday departure
1,745 mi · 48h avg
How It Works
1
Lane lookup
Lambda reads lane_averages.csv from Object store scv-data-curated on cold start, caches in-memory.
2
ETA calculation
Adds avg_transit_hours to depart_time and returns predicted_eta as ISO 8601 timestamp.
3
Access control layer
x-api-key header validated against SHA-256 hash stored in scv-keys-store Object store bucket.
Service model
Service layerServerless workflow endpoint
Operating dataLane baseline dataset
Access controlScoped demo key
Prediction methodLane-average baseline